Neural Network Synapse Augmentation via Predefined Bias
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Solution Overview
Problem
Existing neural networks face challenges in implementing augmentation or suppression to enhance contextual and environmental awareness, leading to constrained performance in understanding input data over time, due to increased training time, cost, complexity, and power consumption when incorporating cross-contextual information.
Innovation Solution
Incorporating intentionally added predefined bias into the training dataset and input content to modulate the output of neural networks, allowing for activation or suppression of synapses without exponentially increasing training complexity, while leveraging existing neural network architectures and datasets.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If cross-contextual information is used during training to achieve contextual and environmental awareness, then the neural network's performance and understanding capability are improved, but the training time, cost, complexity, and power consumption increase exponentially
Solution Approach 1:
The patent segments the training process into two distinct phases: first training the neural network on base content without contextual information, then separately training on contextual information. This division allows each training phase to be optimized independently, avoiding the exponential complexity that would result from training on all contextual combinations simultaneously.
Solution Approach 2:
The patent applies preliminary action by first establishing the base neural network training on primary content before introducing contextual information. The base layer is trained and stabilized first, creating a foundation that can later be enhanced with contextual awareness without requiring complete retraining on all possible contextual combinations.
2Adaptability or versatility
If cross-contextual information is incorporated during training to enable augmentation or suppression, then the neural network achieves sophisticated understanding of input data, but the training dataset size and processing requirements increase exponentially
Solution Approach 1:
The training dataset is segmented into base content and contextual information components. The neural network is first trained on base content to establish fundamental recognition capabilities, then separately trained on contextual information to add environmental awareness. This segmentation avoids the need to create and process exponentially larger datasets containing all possible contextual combinations.
Solution Approach 2:
The patent creates a universal training approach where the neural network learns to process both base content and contextual information through the same architectural framework. The base layer and contextual layer use consistent processing mechanisms, allowing the system to handle various types of contextual information without requiring separate specialized training for each context type.
3Reliability
If the neural network architecture is changed to implement augmentation or suppression, then contextual awareness can be achieved, but compatibility with existing standard tools and architectures is lost
Solution Approach 1:
The patent implements a nested architecture where a contextual processing layer is added around an existing base neural network layer. The base layer maintains its original architecture and can be any standard neural network model, while the contextual layer wraps around it to provide augmentation and suppression capabilities. This nesting allows the system to gain contextual awareness while preserving compatibility with existing standard tools and architectures.
Solution Approach 2:
The patent introduces an intermediary contextual processing layer that sits between the input data and the base neural network. This intermediary layer processes contextual information and modulates the base network's output through augmentation or suppression, acting as a mediator that enables contextual awareness without requiring fundamental changes to the underlying base network architecture.
Data Source
AI summary
A computer system (which may include one or more computers) that trains a neural network is described. During operation, the computer system may obtain content. Then, the computer system may train the neural network using a training dataset having content, where at least a subset of the content includes intentionally added predefined bias, and where the intentionally added predefined bias modulates an output of the neural network. Note that the modulated output may correspond to activation or suppression of one or more synapses in the neural network. For example, the activation or suppression may adjust weights associated with the one or more synapses for a predefined time interval. Moreover, the intentionally added predefined bias may include additional content that leverages associated learning with one or more features in at least the subset of the content and that are different from the additional content.